Cultivar identification of sweet cherries based on texture parameters determined using image analysis

Abstract

The aim of this study was to develop discriminative models for distinguishing different cultivars of whole sweet cherries based on the texture parameters determined using image analysis. The whole fruit images of “Büttner's Red,” “Kordia,” and “Lapins” were acquired using a digital camera. The discriminative models were built for textures selected from individual color channels R, G, B, L, a, b, X, Y, and Z and color spaces CIE RGB (R—red, B—blue, G—green), CIE Lab (L*—lightness from black to white, a*—green and red, b*—blue and yellow), CIE XYZ (Y—lightness, X and Z—components of color information). The models were developed for texture sets without division and subsets of textures with division into those calculated based on the histogram, co‐occurrence matrix, run‐length matrix, autoregressive model, gradient map. The total accuracy reached 100% for the models built based on sets of textures without division selected from color channels R and X and color spaces RGB, Lab, and XYZ. In the case of divided sets of textures, the correctness of 100% was obtained for textures selected from histogram and co‐occurrence matrix for color space Lab. For color channels, the highest accuracy was equal to 97% for the model built based on the selected textures calculated based on the histogram for color channel L. The significance of this study is great for practical applications. The correct identification of sweet cherry cultivar using the discriminative models can be important for the selection of fruits with the desired properties for consumption and processing. Practical applications The research involved the use of a procedure that allows the classification of sweet cherries in a non‐destructive, objective, and inexpensive manner. The obtained results were very satisfactory and allowed for the complete discrimination of sweet cherry cultivars based on texture parameters of the whole fruit. The developed models may be used in practice for cultivar identification of sweet cherries and detection of falsification or confirmation of authenticity. It will avoid the mixing of different cultivars with different properties intended for use and processing.

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

“…In 2021, Ropelewska completed the study of cherry variety discrimination using the acquired cherry images ( Ropelewska et al., 2021 ). The research proves that the discriminant model based on texture parameters obtained from different color channels and texture parameters obtained from different color spaces has high recognition accuracy for cherry varieties.…”

Metabolomics Reveals Specific Metabolic Changes in Sweet Cherries (Prunus avium L.) Subjected to Postharvest Treatment with Melatonin after Mechanical Stress

“…Loss of firmness is the next most common issue, making up 10.5% of the damage, followed by fruit splitting at 2.4%. Although several recent studies have used image processing as a fast and economical alternative to segregate cherries and/or pits of different cultivars according to texture and color parameters [3][4][5], it is difficult to use these technologies for the early detection of damage or defects that appear during cold storage (more than one week after harvest), such as surface pitting. Low-temperature storage is necessary to preserve the quality attributes of various fruits; however, fruits might develop symptoms of chilling injury, leading to oxidative damage and lipid peroxidation.…”